The Rapid Rise of Computer Vision Technology

Computer vision is just one of the applications for machine learning experiencing rapid growth and increasing popularity in recent years. Machine learning technologies that are on the rise include predictive analytics, natural language processing, sentiment analysis, graph databases and graph analysis. Wikipedia defines computer vision as:

... a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. ... Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, learning, indexing, motion estimation, and image restoration.

While it seems that computer vision is used primarily for building image-recognition platforms and applications, computer vision technology can be used for a variety of use cases across many industries. In a recent ProgrammableWebarticle, CamFind API CEO Dominik Mazur provides specific examples such as object recognition, search and e-commerce, surveillance and security, astronomy and outer space applications, and panoramic photography.

Computer vision has been in the news quite often in recent months. In February, Google launched Project Tango Smartphone, a project that uses 3-D sensing, machine learning, computer vision and other technology to achieve the goal of "giving mobile devices a human-scale understanding of space and motion." Project Tango devices are equipped with customized hardware and software that tracks the motion of the device in 3-D and at the same time creates a map of the environment. According to the Project Tango website, the sensors in Project Tango devices "allow the device to make over a quarter million 3-D measurements every second, updating its position and orientation in real time, combining that data into a single 3-D model of the space around you."

Earlier this month. Google acquired Jetpac, a startup that has created a mobile application that uses computer vision and other machine learning technologies to extract and analyze data from public Instagram photos. The data generated from the photos is used to create city guides. At the time of this writing, the Jetpac City Guides app, which is available only on the iPhone, is a visual guide for more than 6,000 cities worldwide. On Aug. 15, Berlin-based company EyeEm announced the acquisition of Sight.io, a computer vision and machine learning technologies startup that has developed a platform featuring automated photo managing and processing capabilities.

Even the Walt Disney Co. has a division focused on the research and development of advanced technologies, which includes computer graphics, video processing, computer vision, data mining, machine learning and wireless communications. Disney Research consists of research laboratories located in several cities around the world, and its goal is to "positively impact profitability of The Walt Disney Company by inventing technologies and making discoveries that are novel on a global scale."

Below are a few examples of companies using computer vision technology, most of which specialize in image-recognition platforms and applications. A few of these companies also provide platforms that feature natural language processing capabilities. These companies were chosen to show a sampling of the market and also because they provide APIs.

AlchemyAPI

The AlchemyVision API demo shows how the platform can be used for image tagging and visual search.

AlchemyAPI specializes in advanced text and image analysis, and the company provides a cloud-based platform that features advanced NLP and image-recognition functionality. Using AlchemyAPI's API, developers can incorporate sentiment analysis, keyword extraction, entity extraction, image tagging and other NLP and image-recognition features into their applications.

Back in May, ProgrammableWebreported that AlchemyAPI had launched a product line called AlchemyVision, which features a new computer vision API. In March, the company launched Taxonomy and Sentiment Analysis APIs, and in a ProgrammableWebarticle covering the news, AlchemyAPI CEO Elliot Turner hinted that the company plans to debut in the near future APIs that use deep-learning based technology.

Diffbot

Diffbot is a cloud-based data mining platform that uses computer vision, machine learning, NLP and other technologies to understand and extract data from Web pages, including Web pages from news article sites, e-commerce sites and image galleries. Diffbot provides developers with several APIs that can be used to extract data from Web pages, which can then be used to build data-driven applications. Diffbot developer products include a Frontpage API, Page Classifier API, Article API, Product API and other data-extraction tools.

In August 2013, ProgrammableWebreported that Diffbot had released its Product API, which the company had been developing for two years. The Product API is capable of extracting from e-commerce Web pages data such as product title, model number, SKU, regular price, sales price and image. Earlier this year, the company released API client libraries for more than 35 programming languages.

IMRSV

The Cara Cloud demo shows a very basic example of facial-detection analysis from an uploaded photo. Photo from Google Images.

IMRSV, formerly Immersive Labs, specializes in technology that is capable of analyzing facial expressions via any webcam. Using computer vision, the IMRSV Cara platform can measure facial reactions such as smiles, frowns and surprise.

The IMRSV Cara API allows programmatic access to the platform, which developers can use to incorporate facial-detection analysis functionality into their applications. Applications can upload images and videos to the platform, which will then return facial-detection analysis information. The API is particularly useful for embedded, wearable and research analytics. A demo on the IMRSV website demonstrates how the facial-detection analysis works for both images and video.

Khronos OpenVX

The Khronos Group focuses on creating open standards for parallel computing. Open standards established by the group include OpenGL and WebGL. Image credit: Khronos Group.

The Khronos Group is a nonprofit consortium of media-centric technology companies that focuses on developing Khronos API specifications and creating open standard APIs to "enable the authoring and acceleration of graphics, vision, sensor processing and dynamic media on a wide variety of platforms and devices." The Khronos Group is responsible for the creation and development of a variety of open standards, including OpenGL, WebGL, OpenCL and WebCL. In May 2013, ProgrammableWebreported that the group was beginning work on the development of an open API for advanced control of mobile and embedded cameras and sensors.

In November of last year, the Khronos Group released to the public the OpenVX 1.0 provisional specification, an open standard for enabling computer vision algorithms specifically for use cases such as face, body and gesture tracking, automatic driver assistance systems, and object and scene reconstruction. The OpenVX specification features the OpenVX Hardware Acceleration API designed for computer vision applications and libraries.

Lambda Labs

Demos for Lambda Labs facial-recognition and facial-detection technology can be found on the company's website. Photo from Google Images.

Lambda Labs is a computer vision and artificial intelligence technology company based in San Francisco. The company launched the beta version of its open source Face Recognition API back in September 2012. The Lambda Labs Face Recognition API provides facial recognition, facial detection, gender classification and other facial attributes from photos. The company is also developing a Google Glass facial-recognition application despite Google's strict policy regarding facial-recognition and voice-print use cases.

Last year, ProgrammableWeb's Ajay Ohri interviewed Lambda Labs founder Stephen Balaban, who said that Lambda Labs facial-recognition technology is being used primarily by ad agencies and Web and mobile application developers. Balaban also told ProgrammableWeb the following:

The massive centralization of personal information by both enterprise and government is making citizens ask important questions about what privacy means going forward. I've discussed with others in the industry about standardized protocols that may help individuals maintain control over their privacy. Think Robot Exclusion Standard (robots.txt) for face-recognition systems.

Orbeus

Celebrity recognition is a new Rekognition platform feature; a demo is available. Photo from Google Images.

Orbeus is a computer vision company that has developed an integrated visual recognition engine capable of detecting, recognizing and analyzing faces, scenes and objects all together in photos. Orbeus provides developers with access to the cloud-based Rekognition open API platform, which makes it possible to incorporate computer vision functionality into third-party applications. The Orbeus Rekognition API features facial- and concept-recognition capabilities; it is able to detect and recognize faces as well as recognize scenes, landmarks and other objects.

Last year, the company launched visual recognition APIs for Google Glass, which do not violate Google's policy regarding facial-recognition use cases. The APIs for Google Glass are not capable of detecting a person's identity. In a TechCrunch article covering the launch of the Orbeus Rekognition for Glass APIs, Orbeus CEO Ning Xu is quoted as saying, "Our API actually offers different face detection, face reading and scene understanding, so we’re not just a facial-recognition company. But even without facial recognition, we can do a lot of things with your face without revealing your identity."

Conclusion

Computer vision is just the tip of the iceberg when it comes to the advances being made in the field of machine learning technology. Some technology experts believe data science, which includes machine learning, represents "a larger potential disruption than the industrial revolution." Whether this belief is proved to be true or false is only a matter of time.

About the author:Janet Wagner
is a data journalist, developer, and contributor to ProgrammableWeb covering breaking news, in-depth analysis, features, and reviews. She also writes about machine learning, deep learning, GIS and spatial analysis, mapping, and many other technologies. Follow her on Twitter: @webcodepro, Google+, or send her an email.